Sometimes it's necessary to simply ignore color information and represent resulting image in grayscale. Colors in an image get converted to a shade of gray by calculating the effective brightness or luminance of the color and using this value to create a shade of gray that matches the desired brightness. Checking this option results in following raster imagery:

Color image with polygon overlay

Grayscale image with polygon overlay

Image Drawing Order

This option affects drawing order of the raster image inserted into AutoCAD. Sending image to back means positioning it below all other entities in the drawing. This equals to selecting specified image and first issuing DRAWORDER command in AutoCAD, then picking BACK option:

Send to back turned on

Send to back turned off

Image Interpolation

Image interpolation occurs in all imported raster imagery at some stage - whether this be in raster tile stitching or in raster scaling. It happens while TopoPlanner resizes (scales) or remaps (distorts) imported image from one pixel grid to another. Image resizing is necessary when you need to increase or decrease the total number of pixels, whereas remapping can occur under a wider variety of scenarios. There's following image interpolation modes (algorithms) available:

Default

Low

High

Bilinear

Bicubic

Nearest Neighbor

High Quality Bilinear

High Quality Bicubic

Even if the same image resize or remap is performed, the results can vary significantly depending on the interpolation algorithm. It is only an approximation, therefore an image will always lose some quality each time interpolation is performed. Image interpolation works in two directions, and tries to achieve a best approximation of a pixel's color and intensity based on the values at surrounding pixels. The following example illustrates how resizing / enlargement works:

Raster image resizing with and without 2D interpolation

Most popular interpolation algorithms are explained below, on the square [0,3] X [0,3] consisting of 9 unit squares patched together. Color indicates function value. The black dots are the locations of the prescribed data being interpolated:

Nearest Neighbor Interpolation also known as proximal interpolation or, in some contexts, point sampling) is a simple method of multivariate interpolation in one or more dimensions. It is the most basic interpolation method and requires the least processing time of all the interpolation algorithms because it only considers one pixel — the closest one to the interpolated point. This has the effect of simply making each pixel bigger.

Bilinear Interpolation considers the closest 2x2 neighborhood of known pixel values surrounding the unknown pixel. It then takes a weighted average of these 4 pixels to arrive at its final interpolated value. The key idea is to perform linear interpolation first in one direction, and then again in the other direction. Although each step is linear in the sampled values and in the position, the interpolation as a whole is not linear but rather quadratic in the sample location. This results in much smoother looking images than nearest neighbor.

Bicubic Interpolation goes one step beyond bilinear by considering the closest 4x4 neighborhood of known pixels — for a total of 16 pixels. Since these are at various distances from the unknown pixel, closer pixels are given a higher weighting in the calculation. Bicubic produces noticeably sharper images than the previous two methods, and is perhaps the ideal combination of processing time and output quality. For this reason it is a standard in many image editing programs (including Adobe Photoshop), printer drivers and in-camera interpolation.

Nearest neighbor interpolation

Bilinear interpolation

Bicubic interpolation

Image Smoothing

During image interpolation post-processing phase each tile may also get smoothed using specified algorithm. There's following image smoothing modes (algorithms) available:

None

Default

High Speed

High Quality

Anti-alias

Anti-aliasing is a process which attempts to minimize the appearance of aliased or jagged diagonal edges. These give text or images a rough digital appearance. Anti-aliasing removes jagged edges and gives the appearance of smoother edges and higher resolution. It works by taking into account how much an ideal edge overlaps adjacent pixels. The aliased edge simply rounds up or down with no intermediate value, whereas the anti-aliased edge gives a value proportional to how much of the edge was within each pixel: